Financial Forecasting Using Support Vector Machines

The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE), Mean Absolute Error (MAE), Directional Symmetry (DS), Correct Up (CP) trend and Correct Down (CD) trend. S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial time series.     

FAST馈源支撑系统三维可视化模型的创建

国产操作系统因其安全可靠被许多特定的企业和单位使用。中标麒麟系统是国产操作系统中较为成熟的一个系统。Blender是一款跨平台的三维动画制作软件,功能强大且可以在任何主流的操作系统上工作。在中标麒麟系统下,用Blender建模软件对"天眼"FAST中的一部分——馈源支撑系统的三维模型进行了创建,并选取其中的一部分,对其模型的创建过程进行了详细地介绍。     

Using autoencoders for session-based job recommendations

User Modeling and User-Adapted Interaction - In this work, we address the problem of providing job recommendations in an online session setting, in which we do not have full user histories. We...     

Using frame semantics for classifying and summarizing application store reviews

Text mining techniques have been recently employed to classify and summarize user reviews on mobile application stores. However, due to the inherently diverse and unstructured nature of user-generated online textual data, text-based review mining techniques often produce excessively complicated models that are prone to overfitting. In this paper, we propose a novel approach, based on frame semantics, for app review mining. Semantic frames help to generalize from raw text (individual words) to more abstract scenarios (contexts). This lower-dimensional representation of text is expected to enhance the predictive capabilities of review mining techniques and reduce the chances of overfitting. Specifically, our analysis in this paper is two-fold. First, we investigate the performance of semantic frames in classifying informative user reviews into various categories of actionable software maintenance requests. Second, we propose and evaluate the performance of multiple summarization algorithms in generating concise and representative summaries of informative reviews. Three different datasets of app store reviews, sampled from a broad range of application domains, are used to conduct our experimental analysis. The results show that semantic frames can enable an efficient and accurate review classification process. However, in review summarization tasks, our results show that text-based summarization generates more comprehensive summaries than frame-based summarization. Finally, we introduces MARC 2.0, a review classification and summarization suite that implements the algorithms investigated in our analysis.     

Clustering stock price time series data to generate stock trading recommendations: An empirical study

Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.     

Using fuzzy linguistic representations to provide explanatory semantics for data warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses.     

Using a case-based reasoning approach for trading in sports betting markets

The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.     

Using XML for Supplemental Hypertext Support

Our overall research goal is providing hypertext functionality through the WWW to hypertext-unaware information systems with minimal or no changes to the information systems. Information systems dynamically generate their contents and thus require some mapping mechanism to automatically map the generated contents to hypertext constructs (nodes, links, and link markers) instead of hypertext links being hard-coded over static contents. No systematic approach exists, however, for building mapping routines to create useful links that give users direct access to the ISs' primary functionality, give access to metainformation about IS objects, and enable annotation and ad hoc (user-declared) linking. This paper contributes a procedure for analyzing ISs and building mapping routines that supplement information systems with hypertext support. This paper also contributes an eXtensible Markup Language (XML) DTD that declares a set of elements and attributes for representing mapped information in a human-readable, machine-readable, structured, and semantic way. We implemented a prototype to demonstrate the feasibility of using XML to represent mapped information.     

基于案例推理的金融危机预警支持系统

传统方法与模型预测金融危机有较大的局限性,该文提出用基于案例推理方法预测金融危机的思想,并给出基于案例推理的金融危机预警系统 ＣＢＲＦＣＰＳＳ的原型,研究了 ＣＢＲＦＣＰＳＳ中的关键技术：案例的知识表达、案例检索和案例学习等。文章最后给出了应用原型系统进行金融危机预警的部分研究成果。     

谈.Net环境下FastReport控件在财务报表打印开发中的应用

高校财务发票的打印常常因为打印机的不同,而需要调整其格式,由于在.net环境下,Crystal Reports存在着某些不足,该文借用了Delphi自带的FastReport控件。在.net环境下实现其对发票的打印。     

智能财务分析与决策支持软件的现状与发展

财务软件在经历多年发展之后,决策支持软件（DSS）的引入使财务软件应用达到了一个新的高度。本文通过分析决策支持软件（DSS）的现状与发展,并以实际的软件对比阐述了智能财务软件的发展方向。     

智能财务分析与决策支持软件的现状与发展

财务软件在经历多年发展之后,决策支持软件(DSS)的引入使财务软件应用达到了一个新的高度。本文通过分析决策支持软件(DSS)的现状与发展,并以实际的软件对比阐述了智能财务软件的发展方向。     

DATAPLAN: An interface generator for database semantics

A model system ( called DATAPLAN) is proposed for generation, access, and control of a shared database by casual users in their own terminology. The DATAPLAN system achieves this goal by planning a dialog which hierarchically defines the possible universe of discourse where casual users can communicate with the database. The plan is executed on the domains of hierarchical semantics, and controlled by pattern matching with a goal and generative backtracking mechanism. To ensure flexible and smooth communication of casual users with the shared database, the system generates individualized user class “fuzzy” interfaces. They are composed of multiplex associations of the attributes and values of the shared database with those of casual user classes in varying degrees.     

Latent Semantic Analysis: five methodological recommendations

The recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher's primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorization, and predictive modeling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed by the researcher who will embark on LSA. We examine the dilemmas, present the choices, and discuss the considerations under which good methodological decisions are made. We illustrate these issues with the help of four small studies, involving the analysis of abstracts for papers published in the European Journal of Information Systems.     

Bialgebras for structural operational semantics: An introduction

Bialgebras and distributive laws are an abstract, categorical framework to study various flavors of structural operational semantics. This paper aims to introduce the reader to the basics of bialgebras for operational semantics, and to sketch the state of the art in this research area.     

Dataplan: An interface generator for database semantics

A model system (called DATAPLAN) is proposed for generation, access, and control of a shared database by casual users in their own terminology. The DATAPLAN system achieves this goal by planning a dialog which hierarchically defines the possible universe of discourse where casual users can communicate with the database. The plan is executed on the domains of hierarchical semantics, and controlled by pattern matching with a goal and generative backtracking mechanism. To ensure flexible and smooth communication of casual users with the shared database, the system generates individualized user class “fuzzy” interfaces. They are composed of multiplex associations of the attributes and values of the shared database with those of casual user classes in varying degrees.     

ε-Descending Support Vector Machines for Financial Time Series Forecasting

This paper proposes a modified version of support vector machines (SVMs), called ε-descending support vector machines (ε-DSVMs), to model non-stationary financial time series. The ε-DSVMs are obtained by incorporating the problem domain knowledge – non-stationarity of financial time series into SVMs. Unlike the standard SVMs which use a constant tube in all the training data points, the ε-DSVMs use an adaptive tube to deal with the structure changes in the data. The experiment shows that the ε-DSVMs generalize better than the standard SVMs in forecasting non-stationary financial time series. Another advantage of this modification is that the ε-DSVMs converge to fewer support vectors, resulting in a sparser representation of the solution. This revised version was published online in June 2006 with corrections to the Cover Date.